www.iastoppers.com [Editorial Notes] Flood Forecasting in India By IASToppers | 2020-10-08 00:00:00 Flood Forecasting in India For IASToppers’ Editorial Simplified Archive, click here Introduction: When the time available to act is just 24 hours regarding flood forecast, there is no clear idea of the area of inundation, its depth, and the accuracy of the forecast in India. These scenarios are often experienced across most flood forecast river points in Assam, Bihar, Karnataka, Kerala or Tamil Nadu. What are the current flood forecasting systems in India? In India, local agency makes a decision in a flood forecast in a way they merely use the words Copyright © 2021 IASToppers. All rights reserved. | Page 1/4 www.iastoppers.com “Rising” or “Falling” above a water level at a river point. There are many times this happens in India during flood events, when the district administration, municipalities and disaster management authorities receive such forecasts and have to act quickly. Ensemble forecast: In India, there is a form of flood forecast known as the Ensemble forecast that provides a lead time of 7-10 days ahead, with probabilities assigned to different scenarios of water levels and regions of inundation. An example of the probabilities ahead could be something like this: chances of the water level exceeding the danger level is 80%, with likely inundation of a village nearby at 20%. The Ensemble flood forecast certainly helps local administrations with better decision-making and in being better prepared than in a deterministic flood forecast. Multiple agencies in India: The India Meteorological Department (IMD) issues meteorological or weather forecasts while the Central Water Commission (CWC) issues flood forecasts at various river points. The end-user agencies are disaster management authorities and local administrations. Therefore, the advancement of flood forecasting depends on how quickly rainfall is estimated and forecast by the IMD and how quickly the CWC integrates the rainfall forecast (also known as Quantitative Precipitation Forecast or QPF) with flood forecast. Thus, the length of time from issuance of the forecast and occurrence of a flood event termed as “lead time” is the most crucial aspect of any flood forecast to enable risk-based decision-making and undertake cost-effective rescue missions by end user agencies. Outdated technologies and a lack of technological parity between multiple agencies and their poor water governance decrease crucial lead time. What are the issues? IMD has about 35 advanced Doppler weather radars to help it with weather forecasting. Compared to point scale rainfall data from rain gauges, Doppler weather radars can measure the likely rainfall directly from the cloud reflectivity over a large area; thus, the lead time can be extended by up to three days. But the advantage of advanced technology becomes infructuous because most flood forecasts at several river points across India are based on outdated statistical methods (of the type gauge-to-gauge correlation and multiple coaxial correlations) that enable a lead time of less than 24 hours. This is contrary to the perception that India’s flood forecast is driven by Google’s most advanced Artificial Intelligence (AI) techniques. These statistical methods fail to capture the hydrological response of river basins between a base station and a forecast station. They cannot be coupled with QPF too. Google AI has adopted the hydrological data and forecast models derived for diverse river basins across the world for training AI to issue flood alerts in India. This bypasses the data deficiencies and shortcomings of forecasts based on statistical methods. Just as the CWC’s technological gap limits the IMD’s technological advancement, the technological limitations of the IMD can also render any advanced infrastructure deployed by CWC infructuous. Copyright © 2021 IASToppers. All rights reserved. | Page 2/4 www.iastoppers.com The limitations of altitude, range, band, density of radars and its extensive maintenance enlarge the forecast error in QPF which would ultimately reflect in the CWC’s flood forecast. Forecasting errors increase and the burden of interpretation shifts to hapless end user agencies. The outcome is an increase in flood risk and disaster. Government’s efforts: IMD has begun testing and using ensemble models for weather forecast through its 6.8 peta flops supercomputers named Pratyush and Mihir. The forecasting agency has still to catch up with advanced technology and achieve technological parity with the IMD in order to couple ensemble forecasts to its hydrological models. It has to modernise not only the telemetry infrastructure but also raise technological compatibility with river basin-specific hydrological, hydrodynamic and inundation modelling. To meet that objective, it needs a technically capable workforce that is well versed with ensemble models and capable of coupling the same with flood forecast models. It is only then that India can look forward to probabilistic-based flood forecasts with a lead time of more than seven to 10 days and which will place it on par with the developed world. Central Water Commission (CWC) has modernised its flood management system over the years, there are still massive gaps that need to be filled to make it a much more responsive system. Two types of measures are taken for flood protection: Structural (embankments, dams, reservoirs, and natural detention basins), and non-structural (flood forecasting and warning, floodplain zoning). What India can learn? The developed world has shifted from deterministic forecasting towards ensemble weather models that measure uncertainty by causing perturbations in initial conditions, reflecting the different states of the chaotic atmosphere. A study by the National Institute of Technology, Warangal, Telangana shows that it is only recently that India has moved to using hydrological (rainfall-runoff models) capable of being coupled with QPF. The United States which is estimated to have a land area thrice that of India, has about 160 next generation S-band Doppler weather radars with a range of 250-300 km. The United States, the European Union and Japan have already shifted towards Ensemble flood forecasting along with Inundation modelling. India has only recently shifted towards Deterministic forecast i.e. Rising or Falling type forecast per model run. India will need at least an 80-100 S-band dense radar network to cover its entire territory for accurate QPF. Suggestions: On the structural side, the management of reservoirs and dams, maintenance of embankments and data collection on a river’s silt-bearing capacity have to be improved. Copyright © 2021 IASToppers. All rights reserved. | Page 3/4 www.iastoppers.com On the non-structural side, data on river flow and discharge must be enhanced; the installation and maintenance of technical equipment such as gauges have to be expedited. The information on floods is given to the public; it has to be timely, useful and in a non- technical language. An independent evaluation of the flood forecasting system must be put in place to identify the gaps in the system, and ensure that CWC performs its role better than it is doing now. Conclusion: India has a long way to go before mastering ensemble model-based flood forecasting. With integration between multiple flood forecasting agencies, end user agencies can receive probabilistic forecasts, reducing flood hazard across the length and breadth of India. 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